Electrocardiogram analysis for cardiac arrhythmia classification and prediction through self attention based auto encoder
Abstract Sudden cardiac arrest among young people is a recent worldwide risk, and it is noticed that people with cardiac arrhythmia are more susceptible to various heart diseases. Manual classification can be error-prone, and certainly, there is a need for automation to classify ECG signals to predi...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-93906-5 |
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| author | Ameet Shah Dhanpratap Singh Heba G. Mohamed Salil Bharany Ateeq Ur Rehman Seada Hussen |
| author_facet | Ameet Shah Dhanpratap Singh Heba G. Mohamed Salil Bharany Ateeq Ur Rehman Seada Hussen |
| author_sort | Ameet Shah |
| collection | DOAJ |
| description | Abstract Sudden cardiac arrest among young people is a recent worldwide risk, and it is noticed that people with cardiac arrhythmia are more susceptible to various heart diseases. Manual classification can be error-prone, and certainly, there is a need for automation to classify ECG signals to predict cardiac arrhythmia accurately. The proposed self-attention artificial intelligence auto-encoder algorithm proved an effective cardiac arrhythmia classification strategy with a novel modified Kalman filter pre-processing. We achieved 24.00 SNRimp, 0.055 RMSE, 22.1 PRD% for -5db, 20.4 SNRimp, 0.0245 RMSE, 12 PRD% whereas 14.05 SNRimp, 0.010 RMSE, and 7.25 PRD%, which reduces the ECG signal noise during the pre-processing and improves the visibility of the QRS complex and R-R peaks of ECG waveform. The extracted features were used in network of neurons to execute the classification for MIT-BIH arrhythmia databases using the newly developed self-attention autoencoder (AE) algorithm. The results are compared with existing models, revealing that the proposed system outperforms the classification and prediction of cardiac arrhythmia with a precision of 99.91%, recall of 99.86%, and accuracy of 99.71%. It is confirmed that self-attention-AE training results are promising, and it benefits the diagnosis of ECGs for complex cardiac conditions to solve real-world heart problems. |
| format | Article |
| id | doaj-art-bd0214c7e50b45d1aebc1e7a7e66e641 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-bd0214c7e50b45d1aebc1e7a7e66e6412025-08-20T02:52:16ZengNature PortfolioScientific Reports2045-23222025-03-0115112310.1038/s41598-025-93906-5Electrocardiogram analysis for cardiac arrhythmia classification and prediction through self attention based auto encoderAmeet Shah0Dhanpratap Singh1Heba G. Mohamed2Salil Bharany3Ateeq Ur Rehman4Seada Hussen5School of Computer Science and Engineering, Lovely Professional UniversitySchool of Computer Science and Engineering, Lovely Professional UniversityDepartment of Electrical Engineering, College of Engineering , Princess Nourah bint Abdulrahman UniversityChitkara University Institute of Engineering and Technology , Chitkara UniversityComputer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical SciencesDepartment of Electrical Power, Adama Science and Technology UniversityAbstract Sudden cardiac arrest among young people is a recent worldwide risk, and it is noticed that people with cardiac arrhythmia are more susceptible to various heart diseases. Manual classification can be error-prone, and certainly, there is a need for automation to classify ECG signals to predict cardiac arrhythmia accurately. The proposed self-attention artificial intelligence auto-encoder algorithm proved an effective cardiac arrhythmia classification strategy with a novel modified Kalman filter pre-processing. We achieved 24.00 SNRimp, 0.055 RMSE, 22.1 PRD% for -5db, 20.4 SNRimp, 0.0245 RMSE, 12 PRD% whereas 14.05 SNRimp, 0.010 RMSE, and 7.25 PRD%, which reduces the ECG signal noise during the pre-processing and improves the visibility of the QRS complex and R-R peaks of ECG waveform. The extracted features were used in network of neurons to execute the classification for MIT-BIH arrhythmia databases using the newly developed self-attention autoencoder (AE) algorithm. The results are compared with existing models, revealing that the proposed system outperforms the classification and prediction of cardiac arrhythmia with a precision of 99.91%, recall of 99.86%, and accuracy of 99.71%. It is confirmed that self-attention-AE training results are promising, and it benefits the diagnosis of ECGs for complex cardiac conditions to solve real-world heart problems.https://doi.org/10.1038/s41598-025-93906-5Cardiac arrhythmiaSelf-attention mechanismAtrial fibrillationDeep learning classificationArtificial intelligencePrediction |
| spellingShingle | Ameet Shah Dhanpratap Singh Heba G. Mohamed Salil Bharany Ateeq Ur Rehman Seada Hussen Electrocardiogram analysis for cardiac arrhythmia classification and prediction through self attention based auto encoder Scientific Reports Cardiac arrhythmia Self-attention mechanism Atrial fibrillation Deep learning classification Artificial intelligence Prediction |
| title | Electrocardiogram analysis for cardiac arrhythmia classification and prediction through self attention based auto encoder |
| title_full | Electrocardiogram analysis for cardiac arrhythmia classification and prediction through self attention based auto encoder |
| title_fullStr | Electrocardiogram analysis for cardiac arrhythmia classification and prediction through self attention based auto encoder |
| title_full_unstemmed | Electrocardiogram analysis for cardiac arrhythmia classification and prediction through self attention based auto encoder |
| title_short | Electrocardiogram analysis for cardiac arrhythmia classification and prediction through self attention based auto encoder |
| title_sort | electrocardiogram analysis for cardiac arrhythmia classification and prediction through self attention based auto encoder |
| topic | Cardiac arrhythmia Self-attention mechanism Atrial fibrillation Deep learning classification Artificial intelligence Prediction |
| url | https://doi.org/10.1038/s41598-025-93906-5 |
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